library(DendriticSpineR)
spines <- read_spines(file = "kolce dendrytyczne myszy tg3-gm.csv",
animal_col_name = "Animal",
group_col_name = "Group",
photo_col_name = "Photo_ID_rel",
spines_col_name = "spine_number",
properties_col_name = c("length", "area", "length_area_ratio", "length_width_ratio"),
header = TRUE, sep = ";")
length
plot_distributions(spines, property = "length", ecdf = TRUE, x_lim = c(0, 2))
plot_distributions(spines, property = "length", ecdf = FALSE, x_lim = c(0, 2))
plot_animals(spines, property = "length", box = FALSE)
plot_animals(spines, property = "length", box = TRUE)
plot_crossed_effects(spines, property = "length", strat = "Animal:group", mixed = TRUE)
(ms <- model_spines(spines, photo_col_name = "Photo_ID_rel"))
## $lsmeans
## Group lsmean SE df lower.CL upper.CL
## tg dmso -0.2208147 0.02642542 14.22 -0.2774102 -0.1642192
## tg gm -0.3123998 0.02628428 14.06 -0.3687533 -0.2560464
## wt dmso -0.3500326 0.02688921 14.71 -0.4074429 -0.2926222
## wt gm -0.3249601 0.02589163 13.06 -0.3808715 -0.2690487
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## tg dmso - tg gm 0.09158511 0.02667832 151.64 3.433 0.0042
## tg dmso - wt dmso 0.12921784 0.03770056 14.47 3.427 0.0182
## tg dmso - wt gm 0.10414537 0.03699567 13.63 2.815 0.0601
## tg gm - wt dmso 0.03763273 0.03760177 14.39 1.001 0.7514
## tg gm - wt gm 0.01256026 0.03689498 13.55 0.340 0.9858
## wt dmso - wt gm -0.02507247 0.02668953 159.47 -0.939 0.7837
##
## P value adjustment: tukey method for comparing a family of 4 estimates
diffogram(ms)
## Warning: Removed 1 rows containing missing values (geom_segment).
area
plot_distributions(spines, property = "area", ecdf = TRUE, x_lim = c(0, 2))
plot_distributions(spines, property = "area", ecdf = FALSE, x_lim = c(0, 2))
plot_animals(spines, property = "area", box = FALSE)
plot_animals(spines, property = "area", box = TRUE)
plot_crossed_effects(spines, property = "area", strat = "Animal:group", mixed = TRUE)
(ms <- model_spines(spines, photo_col_name = "Photo_ID_rel"))
## $lsmeans
## Group lsmean SE df lower.CL upper.CL
## tg dmso -0.2208147 0.02642542 14.22 -0.2774102 -0.1642192
## tg gm -0.3123998 0.02628428 14.06 -0.3687533 -0.2560464
## wt dmso -0.3500326 0.02688921 14.71 -0.4074429 -0.2926222
## wt gm -0.3249601 0.02589163 13.06 -0.3808715 -0.2690487
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## tg dmso - tg gm 0.09158511 0.02667832 151.64 3.433 0.0042
## tg dmso - wt dmso 0.12921784 0.03770056 14.47 3.427 0.0182
## tg dmso - wt gm 0.10414537 0.03699567 13.63 2.815 0.0601
## tg gm - wt dmso 0.03763273 0.03760177 14.39 1.001 0.7514
## tg gm - wt gm 0.01256026 0.03689498 13.55 0.340 0.9858
## wt dmso - wt gm -0.02507247 0.02668953 159.47 -0.939 0.7837
##
## P value adjustment: tukey method for comparing a family of 4 estimates
diffogram(ms)
## Warning: Removed 1 rows containing missing values (geom_segment).
length_area_ratio
plot_distributions(spines, property = "length_area_ratio", ecdf = TRUE, x_lim = c(0, 2))
plot_distributions(spines, property = "length_area_ratio", ecdf = FALSE, x_lim = c(0, 2))
plot_animals(spines, property = "length_area_ratio", box = FALSE)
plot_animals(spines, property = "length_area_ratio", box = TRUE)
plot_crossed_effects(spines, property = "length_area_ratio", strat = "Animal:group", mixed = TRUE)
(ms <- model_spines(spines, photo_col_name = "Photo_ID_rel"))
## $lsmeans
## Group lsmean SE df lower.CL upper.CL
## tg dmso -0.2208147 0.02642542 14.22 -0.2774102 -0.1642192
## tg gm -0.3123998 0.02628428 14.06 -0.3687533 -0.2560464
## wt dmso -0.3500326 0.02688921 14.71 -0.4074429 -0.2926222
## wt gm -0.3249601 0.02589163 13.06 -0.3808715 -0.2690487
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## tg dmso - tg gm 0.09158511 0.02667832 151.64 3.433 0.0042
## tg dmso - wt dmso 0.12921784 0.03770056 14.47 3.427 0.0182
## tg dmso - wt gm 0.10414537 0.03699567 13.63 2.815 0.0601
## tg gm - wt dmso 0.03763273 0.03760177 14.39 1.001 0.7514
## tg gm - wt gm 0.01256026 0.03689498 13.55 0.340 0.9858
## wt dmso - wt gm -0.02507247 0.02668953 159.47 -0.939 0.7837
##
## P value adjustment: tukey method for comparing a family of 4 estimates
diffogram(ms)
## Warning: Removed 1 rows containing missing values (geom_segment).
length_width_ratio
plot_distributions(spines, property = "length_width_ratio", ecdf = TRUE, x_lim = c(0, 2))
plot_distributions(spines, property = "length_width_ratio", ecdf = FALSE, x_lim = c(0, 2))
plot_animals(spines, property = "length_width_ratio", box = FALSE)
plot_animals(spines, property = "length_width_ratio", box = TRUE)
plot_crossed_effects(spines, property = "length_width_ratio", strat = "Animal:group", mixed = TRUE)
(ms <- model_spines(spines, photo_col_name = "Photo_ID_rel"))
## $lsmeans
## Group lsmean SE df lower.CL upper.CL
## tg dmso -0.2208147 0.02642542 14.22 -0.2774102 -0.1642192
## tg gm -0.3123998 0.02628428 14.06 -0.3687533 -0.2560464
## wt dmso -0.3500326 0.02688921 14.71 -0.4074429 -0.2926222
## wt gm -0.3249601 0.02589163 13.06 -0.3808715 -0.2690487
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## tg dmso - tg gm 0.09158511 0.02667832 151.64 3.433 0.0042
## tg dmso - wt dmso 0.12921784 0.03770056 14.47 3.427 0.0182
## tg dmso - wt gm 0.10414537 0.03699567 13.63 2.815 0.0601
## tg gm - wt dmso 0.03763273 0.03760177 14.39 1.001 0.7514
## tg gm - wt gm 0.01256026 0.03689498 13.55 0.340 0.9858
## wt dmso - wt gm -0.02507247 0.02668953 159.47 -0.939 0.7837
##
## P value adjustment: tukey method for comparing a family of 4 estimates
diffogram(ms)
## Warning: Removed 1 rows containing missing values (geom_segment).